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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Bagus Sujiwo Antoni Wibowo Dewi Retno Sari Saputro BINUS Graduate Program - Master of Computer Science Bina Nusantara University Indonesia Program Studi Matematika FMIPA UNS Universitas Sebelas Maret Indonesia

ISBN: (纸本)9781665401524

This paper presented a sentiment analysis of the Indonesian government's policies in overcoming Covid 19 through twitter data using several classification methods, namely SVM, Naive Bayes, and LSTM. Based on the analysis of the twitter data, it was found that the twitter community in Indonesia gave negative sentiments to government policies in handling Covid 19. From the experimental results, it was found that SVM gave the best sentiment results compared to Naïve Bayes and LSTM by providing an accuracy of 88.5%.

关键词： COVID-19 Government policies Support vector machines Seminars Sentiment analysis Social networking (online) Blogs

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

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Procedia computer science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia computer science 2021年 179卷 81-87页

作者： Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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Database management system design for automatic weather information with twitter data collection 6

Database management system design for automatic weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Perbangsa, Anzaludin Samsinga Sigalingging, Join W. C. Krisna, Adhi Asta Anggrayani, Suci Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Meteorological Climatological and Geophysical Agency Database Center Division of Bmkg Jakarta10720 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information. © 2021 IEEE.

关键词： Database systems

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Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data

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Procedia computer science 2021年 179卷 202-210页

作者： Arif Budiarto Bharuno Mahesworo Alam Ahmad Hidayat Ika Nurlaila Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

关键词： Population Stratification Ancestry Estimation Gaussian Mixture Model Genomics SNPs

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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Sport Field Reservation Based on Mobile Application 7

Sport Field Reservation Based on Mobile Application

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Claudinus, Tonto Prayoga Wicaksana, Made Kornelius Sitorus, Nicolas Ariqi Gustiandza, Marsa Oktavia, Tanty Lumban Gaol, Ford Hosoda, Takaaki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665404228

This paper presents the sports apps based on a mobile application that has a function to help the provider or the owner of the sports field and sports vendor to promote their business in the apps. Due to the popularity and convenience that mobile applications had and high interest from many people in having a healthy life, it is a great opportunity to combine both of them. Therefore, this study focusses to build mobile sports application using object-oriented analysis and design method, that could meet the demand of lots of peoples that wants to do a sport especially a ball sport. Otherwise, the application can help and support the community and public, especially for ball sports fans, to make reservations for the sports field easier without having to come to the location and also meet their sporting needs. © 2020 IEEE.

关键词： Sports

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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IndoBERT for classifying hate speech in Twitter

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AIP Conference Proceedings 2024年第1期3026卷

作者： Hendri Santosa Fatahillah Rachman Stanley Armando Austen Christianto Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Any form of communication that expresses hatred, prejudice, or hostility toward a particular individual or group of people based on attributes such as their race, religion, ethnicity, nationality, gender, sexual orientation, disability, or other protected characteristics is considered hate speech. Hate speech can be verbal, written, or symbolic. Hate speech can take many forms, and it often involves derogatory language, offensive stereotypes, or the incitement of violence or discrimination against the targeted individuals or groups. The content of hate speech is easy found in forum or discussion in social media include twitter. Twitter is a microblogging-based virtual entertainment where clients can peruse and compose text called tweets or tweets. This exploration executes order of disdain discourse in media Twitter utilizing IndoBERT. IndoBERT is the Indonesian form of BERT model utilizing over 220M words. It was a Convolutional Neural Network-based algorithm that had been modified. Th highlight extraction in Transformer isn’t finished by convolution utilizing a part like CNN, however includes an encoder and decoder. The outcome demonstrates IndoBERT’s excellent ability to categorize hate speech.

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The Design of Smart Cashless Transaction 2020

The Design of Smart Cashless Transaction

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5th International Conference on Intelligent Information Technology, ICIIT 2020

作者： Fernando, Anthony Bryan Shreevasta Desprianto, Ferry Gaol, Ford Lumban Oktavia, Tanty Mobile Application and Technology-School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department-School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376594

Cashless Transaction describes as a state where financial transactions are not bound by physical form such as banknotes or coins, but rather trough transfer of digital information (electronic representation of money). Cashless Transaction exists because of fast majorities of human transaction evolved towards digital environment, this creates an opportunity where legal money exists, recorded, and exchanged in digital form. Thus cashless transaction will make less effort in currency operation cost, keep on track with expenses, connected anytime everywhere as long as connected with internet, and eliminates the needs of banknotes or receipt. Cashless Transaction have done over the past few years making life easier by reducing a number of objects to merge into 1 functional object that could make payments faster and simpler starting with check the first mark of cashless transaction that's still being used nowadays, but cashless transaction doesn't stop evolving in this modern era where money is demonetized, cashless transaction is moving into the digital grid resulting a breakthrough mobile wallets and online transfer that offers people flexibility because its technology advancement makes it possible to make payments anytime and anywhere since it's only depend on a secure network(internet) and a platform, therefore cashless transaction could be done as fast as possible saving precious time. Cashless transaction has some of key components such as security, UX, Functionality, Performance and Data Integrity. Five key components could make cashless transaction become reality but also poses a different threat to cashless transaction. Hacking is one of them. Hacking could be done by anyone that have the ability to compromise the transmission, the transmission needs to have more data protection thus the cashless transaction could be done safely and free of risk. © 2020 ACM.

关键词： Personal computing

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